For a natural social human-robot interaction, it is essential for a robot to learn the human-like social skills. However, learning such skills is notoriously hard due to the limited availability of direct instructions from people to teach a robot. In this paper, we propose an intrinsically motivated reinforcement learning framework in which an agent gets the intrinsic motivation-based rewards through the action-conditional predictive model. By using the proposed method, the robot learned the social skills from the human-robot interaction experiences gathered in the real uncontrolled environments. The results indicate that the robot not only acquired human-like social skills but also took more human-like decisions, on a test dataset, than a robot which received direct rewards for the task achievement.
@article{arxiv.1804.05259,
title = {Intrinsically motivated reinforcement learning for human-robot interaction in the real-world},
author = {Ahmed Hussain Qureshi and Yutaka Nakamura and Yuichiro Yoshikawa and Hiroshi Ishiguro},
journal= {arXiv preprint arXiv:1804.05259},
year = {2018}
}